# Social learning strategies modify the effect of network structure on group performance

arXiv: https://arxiv.org/abs/1606.00753 · HTML: https://mlsanity.com/paper/1606.00753/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2016.

## Built on by (most cited first, 10 of 10)

- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs](https://mlsanity.com/paper/2510.19107/) (2025)
- [GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2603.19677/) (2026)
- [Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning](https://mlsanity.com/paper/2204.12371/) (2022)
- [Semantic knowledge guides innovation and drives cultural evolution](https://mlsanity.com/paper/2510.12837/) (2025)
- [Artificial collectives of specialists and generalists excel at different tasks](https://mlsanity.com/paper/2606.20877/) (2026)
- [Communication Heterogeneity and Collective Consensus in Neural Cellular Automata](https://mlsanity.com/paper/2606.21202/) (2026)
- [Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experiments](https://mlsanity.com/paper/2607.14574/) (2026)
- [Discovering Adaptive Transmission Programs for Collective Innovation](https://mlsanity.com/paper/2608.24545/) (2026)
- [From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs](https://mlsanity.com/paper/2609.38516/) (2026)

## Abstract (arXiv)

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.